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cs.CL2024
States Hidden in Hidden States: Implicit Discrete State Representations Emerge in LLMs' Hidden States
Junhao Chen, Shengding Hu, Zhiyuan Liu +1
Large Language Models (LLMs) exhibit emergent abilities that may reveal aspects of their internal mechanisms. We study one such capability: directly performing extended sequences o…
cs.CL2024★ 2 cited
Bench: Extending Long Context Evaluation Beyond 100K Tokens
Xinrong Zhang, Yingfa Chen, Shengding Hu +8
Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction. Despi…
cs.CL2024
Fine-grained Stateful Knowledge Exploration: Effective and Efficient Graph Retrieval with Large Language Models
Dehao Tao, Congqi Wang, Feng Huang +3
Large Language Models (LLMs) have shown impressive capabilities, yet updating their knowledge remains a significant challenge, often leading to outdated or inaccurate responses. A…